\section{Conclusion and Future Work}
\label{sec:conclude}

In this paper, we study a novel problem of data mining and
management for mobile context. To process a  fast and efficient
mobile query, we propose a graph query method(Mquery) by indexing
frequent subgraphs for mobiles. Mquery first extracts the frequent
subgraphs based on minimal support, then it constructs indices for
those frequent subgraphs. Given a query, it will reconstruct both
the query and the original graph based on the indices to simplify
the topology and accelerate the querying speed. Experimental results
on two different types of data sets show that Mquery can greatly
reduce both the query size and the query response time by more than
50\% compared with the baseline methods.

There are many potential future directions of this work. It would be
interesting to further investigate how to apply Mquery to
recommendation or advertising industry. It would be also interesting
to investigate how to conduct mobile social network analysis based
on Mquery.
